PET imaging features strongly correlated with EGFR mutations in non–small cell lung cancer, according to study results presented at the American Association of Physicists in Medicine Annual Meeting.

The findings suggest radiomic features could help predict EGFR mutations and may lead to the development of a noninvasive imaging biomarker, Hugo Aerts, PhD, director of the computational imaging and bioinformatics laboratory at Dana-Farber Cancer Institute, Brigham and Women’s Hospital and Harvard Medical School, said during a press conference.

Hugo Aerts

Prior research has shown PET–based radiomic features — such as tumor shape, texture and uniformity — may help quantify tumor heterogeneity and predict outcome; however, their association with tumor genetics had not been established.

“Cancer is a genetic disease, and the specific mutations that occur in a person’s DNA profile drive distinct processes,” Aerts said during a press conference. “We wanted to investigate how somatic mutations drive the distinct phenotype, and how we can quantify that phenotype on imaging.”

The analysis included 348 patients with NSCLC. All patients underwent imaging scans prior to treatment, and they also underwent molecular testing based on biopsies of tumor tissue to confirm presence of genetic mutations.

They used the Wilcoxon rank–sum test to evaluate the association between feature values and mutation status. They used Noether’s test to assess whether area under the receiver operating curve (AUC) for each association was statistically significant. They corrected all P values for multiple testing by controlling the false discovery rates (FDR) of 10%.

Aerts and colleagues observed no significantly distinctive imaging features between KRAS–positive and KRAS–negative tumors.

“EGFR–mutated tumors had very distinct phenotypes,” Aerts said. “They were smaller and more compact than EGFR wild-type tumors.”

The result suggests EGFR mutations may cause different imaging phenotypes — or metabolic imaging patterns — that are quantified by radiomic features.

The approach could be applied to several other types of imaging, Aerts said.

In a follow-up study, researchers are evaluating whether PET–based radiomic features can be combined with those derived from other imaging tests — such as MRI or CT — to improve the accuracy of genetic mutation identification in lung and brain cancers.

“Almost every cancer is being imaged, and all of these imaging data are sitting in our hospitals” he said. “We’re now mining all of those data. ... This has very large retrospective potential. All of these patients have already been imaged. We can quantify the phenotype and see how important it is.” – by Mark Leiser

Disclosure: One study researcher reports a consultant role with Amgen.

PET imaging features strongly correlated with EGFR mutations in non–small cell lung cancer, according to study results presented at the American Association of Physicists in Medicine Annual Meeting.

The findings suggest radiomic features could help predict EGFR mutations and may lead to the development of a noninvasive imaging biomarker, Hugo Aerts, PhD, director of the computational imaging and bioinformatics laboratory at Dana-Farber Cancer Institute, Brigham and Women’s Hospital and Harvard Medical School, said during a press conference.

Hugo Aerts

Prior research has shown PET–based radiomic features — such as tumor shape, texture and uniformity — may help quantify tumor heterogeneity and predict outcome; however, their association with tumor genetics had not been established.

“Cancer is a genetic disease, and the specific mutations that occur in a person’s DNA profile drive distinct processes,” Aerts said during a press conference. “We wanted to investigate how somatic mutations drive the distinct phenotype, and how we can quantify that phenotype on imaging.”

The analysis included 348 patients with NSCLC. All patients underwent imaging scans prior to treatment, and they also underwent molecular testing based on biopsies of tumor tissue to confirm presence of genetic mutations.

They used the Wilcoxon rank–sum test to evaluate the association between feature values and mutation status. They used Noether’s test to assess whether area under the receiver operating curve (AUC) for each association was statistically significant. They corrected all P values for multiple testing by controlling the false discovery rates (FDR) of 10%.

Aerts and colleagues observed no significantly distinctive imaging features between KRAS–positive and KRAS–negative tumors.

“EGFR–mutated tumors had very distinct phenotypes,” Aerts said. “They were smaller and more compact than EGFR wild-type tumors.”

The result suggests EGFR mutations may cause different imaging phenotypes — or metabolic imaging patterns — that are quantified by radiomic features.

The approach could be applied to several other types of imaging, Aerts said.

In a follow-up study, researchers are evaluating whether PET–based radiomic features can be combined with those derived from other imaging tests — such as MRI or CT — to improve the accuracy of genetic mutation identification in lung and brain cancers.

“Almost every cancer is being imaged, and all of these imaging data are sitting in our hospitals” he said. “We’re now mining all of those data. ... This has very large retrospective potential. All of these patients have already been imaged. We can quantify the phenotype and see how important it is.” – by Mark Leiser